Extracting and Representating Causal Relations in Children’s Stories
Stories are an essential part of knowledge and communication for humans. They are composed of a series of related concepts, such as events and states, which people use to share ideas to other members of society. Past researches have already tried to replicate the way humans produce or understand stories through creativetext generation systems. Unfortunately, there is a lack of data concerning relationships between events within and across sentences in a story because of lacking common sense knowledge. Therefore, a system called Eventure, which extracts instances of event relations within children’s stories, has been implemented. This system identifies concepts, as well as meta-data, in stories through the use of a thirdparty language processing tool that provides preprocessing capabilities like tokenization and POS tagging. With the concepts and meta-data collected, Eventure utilizes a predefined list of grammar templates and rules to extract instances of event relations and ultimately produces an ontology that stores them. The initial list grammar rules were collected from (Samson 2014) and were modified to accommodate meta-data of concepts. A new event relation between a causing state and a resulting event was also added. To validate the system’s accuracy, a gold standard of the extracted instances of event relations was created using ten children’s stories. The system yielded a precision of only 3.27%, a recall of 10.14%, and an F-measure of 4.95%. This is due to the relatively generic extraction templates, complexity of the children’s stories, and inherent problems with the utilized POS tagger.
Keywords: causal relation, relation extraction, knowledge representation, lexical semantics